Overview

Dataset statistics

Number of variables22
Number of observations11218
Missing cells21772
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory176.0 B

Variable types

DateTime2
Numeric12
Categorical6
Text2

Alerts

latitude is highly overall correlated with longitude and 4 other fieldsHigh correlation
longitude is highly overall correlated with latitude and 4 other fieldsHigh correlation
depth is highly overall correlated with dmin and 1 other fieldsHigh correlation
mag is highly overall correlated with nst and 2 other fieldsHigh correlation
nst is highly overall correlated with latitude and 3 other fieldsHigh correlation
gap is highly overall correlated with nstHigh correlation
dmin is highly overall correlated with longitude and 5 other fieldsHigh correlation
rms is highly overall correlated with depth and 3 other fieldsHigh correlation
horizontalError is highly overall correlated with dmin and 2 other fieldsHigh correlation
depthError is highly overall correlated with dmin and 1 other fieldsHigh correlation
magNst is highly overall correlated with nstHigh correlation
magType is highly overall correlated with magSourceHigh correlation
net is highly overall correlated with latitude and 4 other fieldsHigh correlation
status is highly overall correlated with net and 2 other fieldsHigh correlation
locationSource is highly overall correlated with latitude and 4 other fieldsHigh correlation
magSource is highly overall correlated with latitude and 5 other fieldsHigh correlation
magType is highly imbalanced (59.8%)Imbalance
type is highly imbalanced (92.8%)Imbalance
nst has 3050 (27.2%) missing valuesMissing
gap has 3050 (27.2%) missing valuesMissing
dmin has 5379 (47.9%) missing valuesMissing
place has 479 (4.3%) missing valuesMissing
horizontalError has 3678 (32.8%) missing valuesMissing
magError has 3074 (27.4%) missing valuesMissing
magNst has 3060 (27.3%) missing valuesMissing
time has unique valuesUnique
id has unique valuesUnique
updated has unique valuesUnique
depth has 156 (1.4%) zerosZeros

Reproduction

Analysis started2023-06-26 11:58:40.020888
Analysis finished2023-06-26 11:59:49.117047
Duration1 minute and 9.1 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

time
Date

Distinct11218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
Minimum2023-05-27 11:58:44.607000
Maximum2023-06-26 11:41:18.517000
2023-06-26T08:59:49.735794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:50.157518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

Distinct9451
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.827001
Minimum-62.3246
Maximum85.038
Zeros0
Zeros (%)0.0%
Negative506
Negative (%)4.5%
Memory size87.8 KiB
2023-06-26T08:59:50.563681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-62.3246
5-th percentile8.37637
Q135.088042
median44.463583
Q358.241292
95-th percentile63.28801
Maximum85.038
Range147.3626
Interquartile range (IQR)23.15325

Descriptive statistics

Standard deviation19.605415
Coefficient of variation (CV)0.45778164
Kurtosis3.9131393
Mean42.827001
Median Absolute Deviation (MAD)12.590967
Skewness-1.6419052
Sum480433.3
Variance384.37229
MonotonicityNot monotonic
2023-06-26T08:59:50.954130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.18716667 12
 
0.1%
58.18316667 10
 
0.1%
58.183 10
 
0.1%
58.18066667 10
 
0.1%
58.18 9
 
0.1%
58.17716667 9
 
0.1%
58.18916667 9
 
0.1%
58.1765 9
 
0.1%
58.18383333 8
 
0.1%
58.1845 8
 
0.1%
Other values (9441) 11124
99.2%
ValueCountFrequency (%)
-62.3246 1
< 0.1%
-62.0422 1
< 0.1%
-60.9422 1
< 0.1%
-60.7883 1
< 0.1%
-60.6035 1
< 0.1%
-60.5718 1
< 0.1%
-60.4992 1
< 0.1%
-60.4502 1
< 0.1%
-60.4115 1
< 0.1%
-60.3214 1
< 0.1%
ValueCountFrequency (%)
85.038 1
< 0.1%
85.0174 1
< 0.1%
78.5131 1
< 0.1%
78.479 1
< 0.1%
75.8915 1
< 0.1%
71.7347 1
< 0.1%
71.5926 1
< 0.1%
71.4249 1
< 0.1%
71.3928 1
< 0.1%
69.7689 1
< 0.1%

longitude
Real number (ℝ)

Distinct9952
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-118.48838
Minimum-179.9849
Maximum179.9584
Zeros0
Zeros (%)0.0%
Negative10491
Negative (%)93.5%
Memory size87.8 KiB
2023-06-26T08:59:51.391490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-179.9849
5-th percentile-175.58292
Q1-155.14267
median-125.38083
Q3-116.82362
95-th percentile101.65697
Maximum179.9584
Range359.9433
Interquartile range (IQR)38.319042

Descriptive statistics

Standard deviation71.482348
Coefficient of variation (CV)-0.60328571
Kurtosis8.5634708
Mean-118.48838
Median Absolute Deviation (MAD)25.942717
Skewness2.9784751
Sum-1329202.7
Variance5109.726
MonotonicityNot monotonic
2023-06-26T08:59:51.766391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.8225021 8
 
0.1%
-155.282 7
 
0.1%
-122.8238297 7
 
0.1%
-155.2855 7
 
0.1%
-122.8221664 6
 
0.1%
-122.8153305 6
 
0.1%
-122.8160019 6
 
0.1%
-155.3085 6
 
0.1%
-155.3181667 6
 
0.1%
-155.2755 6
 
0.1%
Other values (9942) 11153
99.4%
ValueCountFrequency (%)
-179.9849 1
< 0.1%
-179.9787 1
< 0.1%
-179.9733 1
< 0.1%
-179.9681 1
< 0.1%
-179.9636 1
< 0.1%
-179.9437 1
< 0.1%
-179.9389 1
< 0.1%
-179.9325 1
< 0.1%
-179.9316 1
< 0.1%
-179.9159 1
< 0.1%
ValueCountFrequency (%)
179.9584 1
< 0.1%
179.921 1
< 0.1%
179.9085 1
< 0.1%
179.8862 1
< 0.1%
179.8487 1
< 0.1%
179.8383 1
< 0.1%
179.812 1
< 0.1%
179.7818 1
< 0.1%
179.7803 1
< 0.1%
179.7566 1
< 0.1%

depth
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3856
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.479915
Minimum-3.2
Maximum630.838
Zeros156
Zeros (%)1.4%
Negative456
Negative (%)4.1%
Memory size87.8 KiB
2023-06-26T08:59:52.172489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-3.2
5-th percentile0
Q12.55
median7.72
Q319.3
95-th percentile102
Maximum630.838
Range634.038
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation52.329351
Coefficient of variation (CV)2.2286857
Kurtosis58.559586
Mean23.479915
Median Absolute Deviation (MAD)5.77
Skewness6.5964825
Sum263397.68
Variance2738.361
MonotonicityNot monotonic
2023-06-26T08:59:52.578631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 418
 
3.7%
0 156
 
1.4%
35 114
 
1.0%
-1.8 66
 
0.6%
5 49
 
0.4%
25.6 35
 
0.3%
6.7 31
 
0.3%
2.6 29
 
0.3%
2.1 27
 
0.2%
7 26
 
0.2%
Other values (3846) 10267
91.5%
ValueCountFrequency (%)
-3.2 2
< 0.1%
-3.019999981 1
 
< 0.1%
-3 4
< 0.1%
-2.96 1
 
< 0.1%
-2.84 1
 
< 0.1%
-2.83 1
 
< 0.1%
-2.67 1
 
< 0.1%
-2.6 2
< 0.1%
-2.54 1
 
< 0.1%
-2.52 1
 
< 0.1%
ValueCountFrequency (%)
630.838 1
< 0.1%
627.666 1
< 0.1%
612.378 1
< 0.1%
608.737 1
< 0.1%
607.923 1
< 0.1%
606.527 1
< 0.1%
602.302 1
< 0.1%
600.468 1
< 0.1%
599.76 1
< 0.1%
591.453 1
< 0.1%

mag
Real number (ℝ)

Distinct562
Distinct (%)5.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.4779852
Minimum-1.59
Maximum7.2
Zeros23
Zeros (%)0.2%
Negative970
Negative (%)8.6%
Memory size87.8 KiB
2023-06-26T08:59:53.000397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.59
5-th percentile-0.232
Q10.7
median1.27
Q31.96
95-th percentile4.4
Maximum7.2
Range8.79
Interquartile range (IQR)1.26

Descriptive statistics

Standard deviation1.2628443
Coefficient of variation (CV)0.85443637
Kurtosis1.1653096
Mean1.4779852
Median Absolute Deviation (MAD)0.63
Skewness1.0754472
Sum16578.56
Variance1.5947758
MonotonicityNot monotonic
2023-06-26T08:59:53.390901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 348
 
3.1%
1.4 316
 
2.8%
1.3 313
 
2.8%
1.2 299
 
2.7%
1.6 264
 
2.4%
1.5 255
 
2.3%
1.8 220
 
2.0%
1 213
 
1.9%
1.9 210
 
1.9%
1.7 185
 
1.6%
Other values (552) 8594
76.6%
ValueCountFrequency (%)
-1.59 1
< 0.1%
-1.34 1
< 0.1%
-1.29 1
< 0.1%
-1.14 1
< 0.1%
-1.11 2
< 0.1%
-1.06 2
< 0.1%
-1.05 1
< 0.1%
-1.04 1
< 0.1%
-1.03 1
< 0.1%
-1.02 1
< 0.1%
ValueCountFrequency (%)
7.2 1
 
< 0.1%
6.4 1
 
< 0.1%
6.3 1
 
< 0.1%
6.2 3
 
< 0.1%
6 4
 
< 0.1%
5.9 4
 
< 0.1%
5.8 5
< 0.1%
5.7 9
0.1%
5.6 8
0.1%
5.5 10
0.1%

magType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size87.8 KiB
ml
7860 
md
2389 
mb
831 
mww
 
94
mwr
 
26
Other values (3)
 
17

Length

Max length5
Median length2
Mean length2.0128377
Min length2

Characters and Unicode

Total characters22578
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowml
2nd rowmd
3rd rowml
4th rowmd
5th rowml

Common Values

ValueCountFrequency (%)
ml 7860
70.1%
md 2389
 
21.3%
mb 831
 
7.4%
mww 94
 
0.8%
mwr 26
 
0.2%
mb_lg 8
 
0.1%
mh 5
 
< 0.1%
mw 4
 
< 0.1%
(Missing) 1
 
< 0.1%

Length

2023-06-26T08:59:53.750130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T08:59:54.164448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ml 7860
70.1%
md 2389
 
21.3%
mb 831
 
7.4%
mww 94
 
0.8%
mwr 26
 
0.2%
mb_lg 8
 
0.1%
mh 5
 
< 0.1%
mw 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
m 11217
49.7%
l 7868
34.8%
d 2389
 
10.6%
b 839
 
3.7%
w 218
 
1.0%
r 26
 
0.1%
_ 8
 
< 0.1%
g 8
 
< 0.1%
h 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22570
> 99.9%
Connector Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 11217
49.7%
l 7868
34.9%
d 2389
 
10.6%
b 839
 
3.7%
w 218
 
1.0%
r 26
 
0.1%
g 8
 
< 0.1%
h 5
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22570
> 99.9%
Common 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 11217
49.7%
l 7868
34.9%
d 2389
 
10.6%
b 839
 
3.7%
w 218
 
1.0%
r 26
 
0.1%
g 8
 
< 0.1%
h 5
 
< 0.1%
Common
ValueCountFrequency (%)
_ 8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 11217
49.7%
l 7868
34.8%
d 2389
 
10.6%
b 839
 
3.7%
w 218
 
1.0%
r 26
 
0.1%
_ 8
 
< 0.1%
g 8
 
< 0.1%
h 5
 
< 0.1%

nst
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct152
Distinct (%)1.9%
Missing3050
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean21.92287
Minimum0
Maximum264
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T08:59:54.540315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median15
Q328
95-th percentile62
Maximum264
Range264
Interquartile range (IQR)20

Descriptive statistics

Standard deviation21.17214
Coefficient of variation (CV)0.96575586
Kurtosis13.196719
Mean21.92287
Median Absolute Deviation (MAD)8
Skewness2.8332619
Sum179066
Variance448.25951
MonotonicityNot monotonic
2023-06-26T08:59:54.946453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 455
 
4.1%
5 445
 
4.0%
7 433
 
3.9%
8 428
 
3.8%
9 376
 
3.4%
4 375
 
3.3%
11 321
 
2.9%
10 307
 
2.7%
12 297
 
2.6%
13 266
 
2.4%
Other values (142) 4465
39.8%
(Missing) 3050
27.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 2
 
< 0.1%
3 29
 
0.3%
4 375
3.3%
5 445
4.0%
6 455
4.1%
7 433
3.9%
8 428
3.8%
9 376
3.4%
10 307
2.7%
ValueCountFrequency (%)
264 1
< 0.1%
239 1
< 0.1%
206 2
< 0.1%
197 1
< 0.1%
196 1
< 0.1%
194 1
< 0.1%
189 1
< 0.1%
175 1
< 0.1%
163 2
< 0.1%
161 1
< 0.1%

gap
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct719
Distinct (%)8.8%
Missing3050
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean119.53314
Minimum14
Maximum339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T08:59:55.336953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile42
Q171
median105
Q3154.2325
95-th percentile246
Maximum339
Range325
Interquartile range (IQR)83.2325

Descriptive statistics

Standard deviation63.54978
Coefficient of variation (CV)0.53164989
Kurtosis0.45122686
Mean119.53314
Median Absolute Deviation (MAD)39
Skewness0.95910825
Sum976346.67
Variance4038.5745
MonotonicityNot monotonic
2023-06-26T08:59:55.711811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 75
 
0.7%
63 73
 
0.7%
103 72
 
0.6%
80 71
 
0.6%
101 71
 
0.6%
69 70
 
0.6%
65 69
 
0.6%
74 68
 
0.6%
62 68
 
0.6%
76 67
 
0.6%
Other values (709) 7464
66.5%
(Missing) 3050
27.2%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 5
< 0.1%
19 2
 
< 0.1%
20 5
< 0.1%
21 6
0.1%
22 5
< 0.1%
23 4
< 0.1%
ValueCountFrequency (%)
339 1
 
< 0.1%
337 3
< 0.1%
335 3
< 0.1%
334 1
 
< 0.1%
333 3
< 0.1%
332 4
< 0.1%
330 4
< 0.1%
329 5
< 0.1%
328 2
 
< 0.1%
327.69 1
 
< 0.1%

dmin
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4790
Distinct (%)82.0%
Missing5379
Missing (%)47.9%
Infinite0
Infinite (%)0.0%
Mean0.67459234
Minimum0
Maximum34.81
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T08:59:56.117951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0059731
Q10.018925
median0.06191
Q30.2415
95-th percentile3.6347
Maximum34.81
Range34.81
Interquartile range (IQR)0.222575

Descriptive statistics

Standard deviation2.0553782
Coefficient of variation (CV)3.0468449
Kurtosis78.908228
Mean0.67459234
Median Absolute Deviation (MAD)0.05102
Skewness7.3870103
Sum3938.9447
Variance4.2245797
MonotonicityNot monotonic
2023-06-26T08:59:56.524055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.013 14
 
0.1%
0.01 13
 
0.1%
0.012 13
 
0.1%
0.017 12
 
0.1%
0.053 9
 
0.1%
0.016 9
 
0.1%
0.011 9
 
0.1%
0.063 8
 
0.1%
0.015 8
 
0.1%
0.014 8
 
0.1%
Other values (4780) 5736
51.1%
(Missing) 5379
47.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.0002818 1
 
< 0.1%
0.000397 1
 
< 0.1%
0.0005998 1
 
< 0.1%
0.0006807 1
 
< 0.1%
0.0009383 1
 
< 0.1%
0.0009863 1
 
< 0.1%
0.001 2
< 0.1%
0.001196 1
 
< 0.1%
0.001254 1
 
< 0.1%
ValueCountFrequency (%)
34.81 1
< 0.1%
32.274 1
< 0.1%
31.959 1
< 0.1%
29.954 1
< 0.1%
29.527 1
< 0.1%
26.475 1
< 0.1%
26.204 1
< 0.1%
24.646 1
< 0.1%
22.385 1
< 0.1%
21.941 1
< 0.1%

rms
Real number (ℝ)

Distinct533
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30215327
Minimum0
Maximum1.85
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T08:59:57.023891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.09
median0.1879
Q30.5
95-th percentile0.83
Maximum1.85
Range1.85
Interquartile range (IQR)0.41

Descriptive statistics

Standard deviation0.27086684
Coefficient of variation (CV)0.89645508
Kurtosis0.24539752
Mean0.30215327
Median Absolute Deviation (MAD)0.1379
Skewness1.0182375
Sum3389.5554
Variance0.073368844
MonotonicityNot monotonic
2023-06-26T08:59:57.523753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 407
 
3.6%
0.07 392
 
3.5%
0.08 337
 
3.0%
0.06 332
 
3.0%
0.09 321
 
2.9%
0.1 318
 
2.8%
0.05 300
 
2.7%
0.03 299
 
2.7%
0.04 278
 
2.5%
0.2 252
 
2.2%
Other values (523) 7982
71.2%
ValueCountFrequency (%)
0 10
 
0.1%
0.001 2
 
< 0.1%
0.0077 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.01 237
2.1%
0.0132 1
 
< 0.1%
0.0138 1
 
< 0.1%
0.0144 1
 
< 0.1%
0.0153 1
 
< 0.1%
0.0156 1
 
< 0.1%
ValueCountFrequency (%)
1.85 1
 
< 0.1%
1.81 1
 
< 0.1%
1.52 1
 
< 0.1%
1.48 1
 
< 0.1%
1.47 1
 
< 0.1%
1.42 1
 
< 0.1%
1.4 1
 
< 0.1%
1.38 1
 
< 0.1%
1.37 2
< 0.1%
1.35 3
< 0.1%

net
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
ak
3050 
av
1795 
nc
1683 
ci
1212 
us
1181 
Other values (10)
2297 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters22436
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowak
2nd rownc
3rd rowak
4th rownc
5th rowak

Common Values

ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1181
 
10.5%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (5) 624
 
5.6%

Length

2023-06-26T08:59:57.945469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1181
 
10.5%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (5) 624
 
5.6%

Most occurring characters

ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22436
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 22436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

id
Text

Distinct11218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
2023-06-26T08:59:58.507790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length10.554733
Min length10

Characters and Unicode

Total characters118403
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11218 ?
Unique (%)100.0%

Sample

1st rowak02384wx0ze
2nd rownc73905551
3rd rowak02384wrwp3
4th rownc73905546
5th rowak02384weeju
ValueCountFrequency (%)
ak02384wx0ze 1
 
< 0.1%
nc73905526 1
 
< 0.1%
ak02384q7vu1 1
 
< 0.1%
hv73462172 1
 
< 0.1%
ak02384wrwp3 1
 
< 0.1%
nc73905546 1
 
< 0.1%
ak02384weeju 1
 
< 0.1%
nc73905541 1
 
< 0.1%
nc73905536 1
 
< 0.1%
nc73905531 1
 
< 0.1%
Other values (11208) 11208
99.9%
2023-06-26T08:59:59.882400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14102
 
11.9%
7 9592
 
8.1%
3 9499
 
8.0%
9 8196
 
6.9%
1 8073
 
6.8%
2 7639
 
6.5%
6 6257
 
5.3%
4 6136
 
5.2%
a 5548
 
4.7%
8 5224
 
4.4%
Other values (26) 38137
32.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77957
65.8%
Lowercase Letter 40446
34.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 5548
13.7%
k 5115
12.6%
c 3523
 
8.7%
n 3055
 
7.6%
v 2918
 
7.2%
u 2433
 
6.0%
s 1795
 
4.4%
i 1722
 
4.3%
h 1185
 
2.9%
w 985
 
2.4%
Other values (16) 12167
30.1%
Decimal Number
ValueCountFrequency (%)
0 14102
18.1%
7 9592
12.3%
3 9499
12.2%
9 8196
10.5%
1 8073
10.4%
2 7639
9.8%
6 6257
8.0%
4 6136
7.9%
8 5224
 
6.7%
5 3239
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 77957
65.8%
Latin 40446
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 5548
13.7%
k 5115
12.6%
c 3523
 
8.7%
n 3055
 
7.6%
v 2918
 
7.2%
u 2433
 
6.0%
s 1795
 
4.4%
i 1722
 
4.3%
h 1185
 
2.9%
w 985
 
2.4%
Other values (16) 12167
30.1%
Common
ValueCountFrequency (%)
0 14102
18.1%
7 9592
12.3%
3 9499
12.2%
9 8196
10.5%
1 8073
10.4%
2 7639
9.8%
6 6257
8.0%
4 6136
7.9%
8 5224
 
6.7%
5 3239
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14102
 
11.9%
7 9592
 
8.1%
3 9499
 
8.0%
9 8196
 
6.9%
1 8073
 
6.8%
2 7639
 
6.5%
6 6257
 
5.3%
4 6136
 
5.2%
a 5548
 
4.7%
8 5224
 
4.4%
Other values (26) 38137
32.2%
Distinct11218
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
Minimum2023-05-27 12:23:04.840000
Maximum2023-06-26 11:46:13.545000
2023-06-26T09:00:00.366565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T09:00:00.913275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

place
Text

Distinct5480
Distinct (%)51.0%
Missing479
Missing (%)4.3%
Memory size87.8 KiB
2023-06-26T09:00:02.162929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length54
Median length48
Mean length26.790763
Min length4

Characters and Unicode

Total characters287706
Distinct characters94
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4490 ?
Unique (%)41.8%

Sample

1st row35 km NNW of Beluga, Alaska
2nd row7km WNW of Cobb, CA
3rd rowSouthern Alaska
4th row6km NW of The Geysers, CA
5th row9km NNE of San Juan Bautista, CA
ValueCountFrequency (%)
of 9633
 
15.6%
km 6574
 
10.6%
alaska 4813
 
7.8%
ca 2861
 
4.6%
nw 1444
 
2.3%
karluk 1183
 
1.9%
nnw 974
 
1.6%
the 866
 
1.4%
geysers 791
 
1.3%
w 720
 
1.2%
Other values (1762) 31966
51.7%
2023-06-26T09:00:03.271931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51086
17.8%
a 23921
 
8.3%
k 17774
 
6.2%
o 16603
 
5.8%
l 12186
 
4.2%
s 11056
 
3.8%
m 10611
 
3.7%
e 10118
 
3.5%
, 10088
 
3.5%
f 10087
 
3.5%
Other values (84) 114176
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 159252
55.4%
Space Separator 51086
 
17.8%
Uppercase Letter 50493
 
17.6%
Decimal Number 16516
 
5.7%
Other Punctuation 10191
 
3.5%
Dash Punctuation 128
 
< 0.1%
Initial Punctuation 28
 
< 0.1%
Final Punctuation 8
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 23921
15.0%
k 17774
11.2%
o 16603
10.4%
l 12186
 
7.7%
s 11056
 
6.9%
m 10611
 
6.7%
e 10118
 
6.4%
f 10087
 
6.3%
n 8282
 
5.2%
i 7318
 
4.6%
Other values (36) 31296
19.7%
Uppercase Letter
ValueCountFrequency (%)
A 9133
18.1%
W 7194
14.2%
N 7192
14.2%
S 5641
11.2%
C 4309
8.5%
E 4183
8.3%
P 1999
 
4.0%
K 1498
 
3.0%
T 1371
 
2.7%
G 1086
 
2.2%
Other values (20) 6887
13.6%
Decimal Number
ValueCountFrequency (%)
1 2845
17.2%
8 2108
12.8%
2 1799
10.9%
5 1785
10.8%
4 1778
10.8%
6 1673
10.1%
3 1543
9.3%
7 1246
7.5%
9 900
 
5.4%
0 839
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 10088
99.0%
. 103
 
1.0%
Space Separator
ValueCountFrequency (%)
51086
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Initial Punctuation
ValueCountFrequency (%)
28
100.0%
Final Punctuation
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 209745
72.9%
Common 77961
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 23921
 
11.4%
k 17774
 
8.5%
o 16603
 
7.9%
l 12186
 
5.8%
s 11056
 
5.3%
m 10611
 
5.1%
e 10118
 
4.8%
f 10087
 
4.8%
A 9133
 
4.4%
n 8282
 
3.9%
Other values (66) 79974
38.1%
Common
ValueCountFrequency (%)
51086
65.5%
, 10088
 
12.9%
1 2845
 
3.6%
8 2108
 
2.7%
2 1799
 
2.3%
5 1785
 
2.3%
4 1778
 
2.3%
6 1673
 
2.1%
3 1543
 
2.0%
7 1246
 
1.6%
Other values (8) 2010
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287311
99.9%
None 358
 
0.1%
Punctuation 36
 
< 0.1%
Latin Ext Additional 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51086
17.8%
a 23921
 
8.3%
k 17774
 
6.2%
o 16603
 
5.8%
l 12186
 
4.2%
s 11056
 
3.8%
m 10611
 
3.7%
e 10118
 
3.5%
, 10088
 
3.5%
f 10087
 
3.5%
Other values (58) 113781
39.6%
None
ValueCountFrequency (%)
ā 230
64.2%
á 65
 
18.2%
ó 8
 
2.2%
í 7
 
2.0%
ū 6
 
1.7%
ī 5
 
1.4%
é 5
 
1.4%
ō 4
 
1.1%
ı 4
 
1.1%
ñ 3
 
0.8%
Other values (13) 21
 
5.9%
Punctuation
ValueCountFrequency (%)
28
77.8%
8
 
22.2%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%

type
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
earthquake
10972 
quarry blast
 
125
explosion
 
73
ice quake
 
44
other event
 
2

Length

Max length14
Median length10
Mean length10.012747
Min length9

Characters and Unicode

Total characters112323
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowearthquake
2nd rowearthquake
3rd rowearthquake
4th rowearthquake
5th rowearthquake

Common Values

ValueCountFrequency (%)
earthquake 10972
97.8%
quarry blast 125
 
1.1%
explosion 73
 
0.7%
ice quake 44
 
0.4%
other event 2
 
< 0.1%
snow avalanche 2
 
< 0.1%

Length

2023-06-26T09:00:03.662384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T09:00:04.037261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
earthquake 10972
96.3%
quarry 125
 
1.1%
blast 125
 
1.1%
explosion 73
 
0.6%
ice 44
 
0.4%
quake 44
 
0.4%
other 2
 
< 0.1%
event 2
 
< 0.1%
snow 2
 
< 0.1%
avalanche 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 22244
19.8%
e 22113
19.7%
r 11224
10.0%
q 11141
9.9%
u 11141
9.9%
t 11101
9.9%
k 11016
9.8%
h 10976
9.8%
s 200
 
0.2%
l 200
 
0.2%
Other values (11) 967
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 112150
99.8%
Space Separator 173
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22244
19.8%
e 22113
19.7%
r 11224
10.0%
q 11141
9.9%
u 11141
9.9%
t 11101
9.9%
k 11016
9.8%
h 10976
9.8%
s 200
 
0.2%
l 200
 
0.2%
Other values (10) 794
 
0.7%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112150
99.8%
Common 173
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22244
19.8%
e 22113
19.7%
r 11224
10.0%
q 11141
9.9%
u 11141
9.9%
t 11101
9.9%
k 11016
9.8%
h 10976
9.8%
s 200
 
0.2%
l 200
 
0.2%
Other values (10) 794
 
0.7%
Common
ValueCountFrequency (%)
173
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 112323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22244
19.8%
e 22113
19.7%
r 11224
10.0%
q 11141
9.9%
u 11141
9.9%
t 11101
9.9%
k 11016
9.8%
h 10976
9.8%
s 200
 
0.2%
l 200
 
0.2%
Other values (11) 967
 
0.9%

horizontalError
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1179
Distinct (%)15.6%
Missing3678
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean1.6900228
Minimum0.08
Maximum35.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T09:00:04.412189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.26
median0.43
Q30.89
95-th percentile9.4
Maximum35.12
Range35.04
Interquartile range (IQR)0.63

Descriptive statistics

Standard deviation3.0722174
Coefficient of variation (CV)1.8178556
Kurtosis7.5759042
Mean1.6900228
Median Absolute Deviation (MAD)0.21
Skewness2.6614742
Sum12742.772
Variance9.4385197
MonotonicityNot monotonic
2023-06-26T09:00:04.802691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.23 198
 
1.8%
0.22 189
 
1.7%
0.24 183
 
1.6%
0.25 172
 
1.5%
0.21 167
 
1.5%
0.26 162
 
1.4%
0.29 150
 
1.3%
0.28 149
 
1.3%
0.2 144
 
1.3%
0.27 144
 
1.3%
Other values (1169) 5882
52.4%
(Missing) 3678
32.8%
ValueCountFrequency (%)
0.08 4
 
< 0.1%
0.09 6
 
0.1%
0.1 16
 
0.1%
0.11 24
 
0.2%
0.12 46
0.4%
0.13 58
0.5%
0.14 69
0.6%
0.15 91
0.8%
0.16 99
0.9%
0.17 93
0.8%
ValueCountFrequency (%)
35.12 1
< 0.1%
21.84 1
< 0.1%
19.57 1
< 0.1%
19.19 1
< 0.1%
17.94 1
< 0.1%
17.33 1
< 0.1%
16.99 1
< 0.1%
16.84 1
< 0.1%
16.79 1
< 0.1%
16.69 1
< 0.1%

depthError
Real number (ℝ)

Distinct1704
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9512318
Minimum0
Maximum124.2
Zeros107
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T09:00:05.208759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.42
median0.73
Q31.47975
95-th percentile7.57015
Maximum124.2
Range124.2
Interquartile range (IQR)1.05975

Descriptive statistics

Standard deviation4.5565086
Coefficient of variation (CV)2.335196
Kurtosis74.63354
Mean1.9512318
Median Absolute Deviation (MAD)0.37
Skewness6.6323324
Sum21888.918
Variance20.76177
MonotonicityNot monotonic
2023-06-26T09:00:05.646119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 515
 
4.6%
0.4 485
 
4.3%
0.2 424
 
3.8%
0.5 405
 
3.6%
0.6 330
 
2.9%
0.7 242
 
2.2%
0.8 235
 
2.1%
0.9 189
 
1.7%
31.61 163
 
1.5%
1 148
 
1.3%
Other values (1694) 8082
72.0%
ValueCountFrequency (%)
0 107
1.0%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 87
0.8%
0.11 3
 
< 0.1%
0.12 11
 
0.1%
0.13 13
 
0.1%
0.14 12
 
0.1%
0.15 20
 
0.2%
0.150000006 1
 
< 0.1%
ValueCountFrequency (%)
124.2 1
 
< 0.1%
53.75 1
 
< 0.1%
41 1
 
< 0.1%
39.8 1
 
< 0.1%
39.4 1
 
< 0.1%
33.8 1
 
< 0.1%
32.454 1
 
< 0.1%
32.23 1
 
< 0.1%
31.61 163
1.5%
29.8 1
 
< 0.1%

magError
Real number (ℝ)

Distinct3348
Distinct (%)41.1%
Missing3074
Missing (%)27.4%
Infinite0
Infinite (%)0.0%
Mean0.24423896
Minimum0
Maximum5.76
Zeros55
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T09:00:06.036639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.053
Q10.115
median0.17486355
Q30.25
95-th percentile0.45419106
Maximum5.76
Range5.76
Interquartile range (IQR)0.135

Descriptive statistics

Standard deviation0.40334154
Coefficient of variation (CV)1.6514218
Kurtosis66.629144
Mean0.24423896
Median Absolute Deviation (MAD)0.065136453
Skewness7.570438
Sum1989.0821
Variance0.16268439
MonotonicityNot monotonic
2023-06-26T09:00:06.427120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 145
 
1.3%
0.2 131
 
1.2%
0.18 94
 
0.8%
0.14 90
 
0.8%
0.23 78
 
0.7%
0.19 77
 
0.7%
0.16 73
 
0.7%
0.15 70
 
0.6%
0.17 69
 
0.6%
0.13 68
 
0.6%
Other values (3338) 7249
64.6%
(Missing) 3074
27.4%
ValueCountFrequency (%)
0 55
0.5%
0.002 1
 
< 0.1%
0.002306002623 1
 
< 0.1%
0.002330902557 1
 
< 0.1%
0.002469337843 1
 
< 0.1%
0.003118965627 1
 
< 0.1%
0.003163165567 1
 
< 0.1%
0.004065631249 1
 
< 0.1%
0.004881416641 1
 
< 0.1%
0.005 1
 
< 0.1%
ValueCountFrequency (%)
5.76 1
< 0.1%
5.27 1
< 0.1%
5.16 1
< 0.1%
5.02 1
< 0.1%
4.97 1
< 0.1%
4.8 1
< 0.1%
4.64 1
< 0.1%
4.63 1
< 0.1%
4.6 1
< 0.1%
4.57 1
< 0.1%

magNst
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct182
Distinct (%)2.2%
Missing3060
Missing (%)27.3%
Infinite0
Infinite (%)0.0%
Mean16.23572
Minimum0
Maximum519
Zeros6
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size87.8 KiB
2023-06-26T09:00:06.833285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q16
median10
Q318
95-th percentile46
Maximum519
Range519
Interquartile range (IQR)12

Descriptive statistics

Standard deviation25.750373
Coefficient of variation (CV)1.5860322
Kurtosis89.139463
Mean16.23572
Median Absolute Deviation (MAD)5
Skewness7.6992861
Sum132451
Variance663.08173
MonotonicityNot monotonic
2023-06-26T09:00:07.239381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 669
 
6.0%
6 664
 
5.9%
4 620
 
5.5%
8 565
 
5.0%
7 537
 
4.8%
9 404
 
3.6%
10 399
 
3.6%
11 369
 
3.3%
3 314
 
2.8%
12 313
 
2.8%
Other values (172) 3304
29.5%
(Missing) 3060
27.3%
ValueCountFrequency (%)
0 6
 
0.1%
1 34
 
0.3%
2 200
 
1.8%
3 314
2.8%
4 620
5.5%
5 669
6.0%
6 664
5.9%
7 537
4.8%
8 565
5.0%
9 404
3.6%
ValueCountFrequency (%)
519 1
< 0.1%
452 1
< 0.1%
436 1
< 0.1%
435 1
< 0.1%
378 1
< 0.1%
373 2
< 0.1%
348 1
< 0.1%
313 2
< 0.1%
277 1
< 0.1%
275 1
< 0.1%

status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
reviewed
9839 
automatic
1379 

Length

Max length9
Median length8
Mean length8.1229274
Min length8

Characters and Unicode

Total characters91123
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
reviewed 9839
87.7%
automatic 1379
 
12.3%

Length

2023-06-26T09:00:07.583044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-26T09:00:07.911066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
reviewed 9839
87.7%
automatic 1379
 
12.3%

Most occurring characters

ValueCountFrequency (%)
e 29517
32.4%
i 11218
 
12.3%
r 9839
 
10.8%
v 9839
 
10.8%
w 9839
 
10.8%
d 9839
 
10.8%
a 2758
 
3.0%
t 2758
 
3.0%
u 1379
 
1.5%
o 1379
 
1.5%
Other values (2) 2758
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 91123
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 29517
32.4%
i 11218
 
12.3%
r 9839
 
10.8%
v 9839
 
10.8%
w 9839
 
10.8%
d 9839
 
10.8%
a 2758
 
3.0%
t 2758
 
3.0%
u 1379
 
1.5%
o 1379
 
1.5%
Other values (2) 2758
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91123
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 29517
32.4%
i 11218
 
12.3%
r 9839
 
10.8%
v 9839
 
10.8%
w 9839
 
10.8%
d 9839
 
10.8%
a 2758
 
3.0%
t 2758
 
3.0%
u 1379
 
1.5%
o 1379
 
1.5%
Other values (2) 2758
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 29517
32.4%
i 11218
 
12.3%
r 9839
 
10.8%
v 9839
 
10.8%
w 9839
 
10.8%
d 9839
 
10.8%
a 2758
 
3.0%
t 2758
 
3.0%
u 1379
 
1.5%
o 1379
 
1.5%
Other values (2) 2758
 
3.0%

locationSource
Categorical

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
ak
3050 
av
1795 
nc
1683 
ci
1212 
us
1181 
Other values (10)
2297 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters22436
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowak
2nd rownc
3rd rowak
4th rownc
5th rowak

Common Values

ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1181
 
10.5%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (5) 624
 
5.6%

Length

2023-06-26T09:00:08.192178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1181
 
10.5%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (5) 624
 
5.6%

Most occurring characters

ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22436
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 22436
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2895
12.9%
n 2543
11.3%
v 2322
10.3%
u 1751
 
7.8%
i 1212
 
5.4%
s 1195
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (8) 1600
 
7.1%

magSource
Categorical

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size87.8 KiB
ak
3050 
av
1795 
nc
1683 
ci
1212 
us
1172 
Other values (13)
2306 

Length

Max length3
Median length2
Mean length2.0008023
Min length2

Characters and Unicode

Total characters22445
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowak
2nd rownc
3rd rowak
4th rownc
5th rowak

Common Values

ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1172
 
10.4%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (8) 633
 
5.6%

Length

2023-06-26T09:00:08.770123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ak 3050
27.2%
av 1795
16.0%
nc 1683
15.0%
ci 1212
 
10.8%
us 1172
 
10.4%
hv 527
 
4.7%
nn 412
 
3.7%
pr 277
 
2.5%
uw 238
 
2.1%
ok 219
 
2.0%
Other values (8) 633
 
5.6%

Most occurring characters

ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2901
12.9%
n 2543
11.3%
v 2322
10.3%
u 1748
 
7.8%
i 1212
 
5.4%
s 1187
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (10) 1614
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22445
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2901
12.9%
n 2543
11.3%
v 2322
10.3%
u 1748
 
7.8%
i 1212
 
5.4%
s 1187
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (10) 1614
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 22445
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2901
12.9%
n 2543
11.3%
v 2322
10.3%
u 1748
 
7.8%
i 1212
 
5.4%
s 1187
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (10) 1614
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4845
21.6%
k 3269
14.6%
c 2901
12.9%
n 2543
11.3%
v 2322
10.3%
u 1748
 
7.8%
i 1212
 
5.4%
s 1187
 
5.3%
h 527
 
2.3%
r 277
 
1.2%
Other values (10) 1614
 
7.2%

Interactions

2023-06-26T08:59:41.756658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:49.900136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:56.393597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:00.945597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:05.384300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:09.966045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:14.325470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:18.863738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:23.407278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:27.958538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:32.827111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:37.284265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:42.144872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:50.338892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:56.772869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:01.326490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:05.767607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:10.330717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:14.720609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:19.250803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:23.808671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:28.358801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:33.208222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:37.659456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:42.508176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:50.753028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:57.152970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:01.716304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:06.135758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:10.692832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:15.091181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:19.629626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:24.192060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:29.028865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:33.584492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:38.027735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:42.884088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:51.213383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:57.530143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:02.064273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:06.485868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:11.046226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:15.460159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:19.993216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:24.576658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:29.413016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:33.943458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:38.400895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:43.222147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:52.069400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:57.872701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:02.424878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:06.811013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:11.384014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:15.808615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:20.377622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:24.925382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:29.779975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:34.291209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:38.743266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:43.584180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:52.655264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:58.240176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:02.779528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:07.147463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:11.744663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:16.185354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:20.739114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:25.279706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:30.140052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:34.653088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:39.092405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:44.041887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:53.529341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:58.627589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:03.147621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:07.516091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:12.112520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:16.562289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:21.135052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:25.674041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:30.523625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:35.041187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:39.466707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:44.406918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:54.184168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:59.006950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:03.533272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:07.891217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:12.478461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:16.957375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:21.513498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:26.057428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:30.908752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:35.411546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:39.854515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:44.804085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:54.832406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:59.392458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:03.893169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:08.262815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:12.859965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:17.342731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:21.906637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:26.449550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:31.307824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:35.803943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:40.242990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:45.187415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:55.240130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:59.779846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:04.305487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:08.646966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:13.241318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:17.741930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:22.290136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:26.840962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:31.712746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:36.185294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:40.643010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:45.552043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:55.629183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:00.202772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:04.677887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:09.257846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:13.606444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:18.112704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:22.661222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:27.221851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:32.077975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:36.557463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:41.016927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:45.881457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:58:56.006559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:00.591684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:05.026554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:09.608886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:13.976896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:18.498715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:23.040087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:27.607188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:32.462387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:36.907620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-26T08:59:41.391495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-26T09:00:09.176257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
latitudelongitudedepthmagnstgapdminrmshorizontalErrordepthErrormagErrormagNstmagTypenettypestatuslocationSourcemagSource
latitude1.000-0.5110.012-0.303-0.6070.259-0.3430.252-0.185-0.2680.321-0.2880.3680.5320.0680.3290.5320.534
longitude-0.5111.0000.0430.2160.337-0.1770.507-0.0360.2110.160-0.3580.1990.3460.5780.0570.2230.5780.580
depth0.0120.0431.0000.4980.4350.0490.6150.5430.4600.117-0.2390.2160.1550.1770.0000.0890.1770.182
mag-0.3030.2160.4981.0000.655-0.1550.6580.5750.4400.163-0.3200.4790.4600.4220.0650.2300.4220.423
nst-0.6070.3370.4350.6551.000-0.5440.2720.424-0.052-0.153-0.2440.7410.2410.2160.0000.0960.2160.219
gap0.259-0.1770.049-0.155-0.5441.0000.258-0.0180.4750.3440.083-0.4930.0820.1990.0350.1410.1990.199
dmin-0.3430.5070.6150.6580.2720.2581.0000.7790.6590.605-0.3010.1090.2080.1510.0000.0910.1510.150
rms0.252-0.0360.5430.5750.424-0.0180.7791.0000.5670.074-0.2440.2320.2280.3270.0640.2190.3270.328
horizontalError-0.1850.2110.4600.440-0.0520.4750.6590.5671.0000.754-0.220-0.0910.3450.3450.0000.1720.3450.345
depthError-0.2680.1600.1170.163-0.1530.3440.6050.0740.7541.000-0.137-0.1320.0180.1480.3120.0420.1480.147
magError0.321-0.358-0.239-0.320-0.2440.083-0.301-0.244-0.220-0.1371.000-0.1910.0600.2070.0000.3630.2070.207
magNst-0.2880.1990.2160.4790.741-0.4930.1090.232-0.091-0.132-0.1911.0000.1440.1090.0000.0770.1090.108
magType0.3680.3460.1550.4600.2410.0820.2080.2280.3450.0180.0600.1441.0000.4910.0270.4230.4910.561
net0.5320.5780.1770.4220.2160.1990.1510.3270.3450.1480.2070.1090.4911.0000.1610.5671.0001.000
type0.0680.0570.0000.0650.0000.0350.0000.0640.0000.3120.0000.0000.0270.1611.0000.0460.1610.161
status0.3290.2230.0890.2300.0960.1410.0910.2190.1720.0420.3630.0770.4230.5670.0461.0000.5670.567
locationSource0.5320.5780.1770.4220.2160.1990.1510.3270.3450.1480.2070.1090.4911.0000.1610.5671.0001.000
magSource0.5340.5800.1820.4230.2190.1990.1500.3280.3450.1470.2070.1080.5611.0000.1610.5671.0001.000

Missing values

2023-06-26T08:59:46.485990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-26T08:59:47.691963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-26T08:59:48.502592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
02023-06-26T11:41:18.517Z61.430500-151.37300063.301.00mlNaNNaNNaN0.35akak02384wx0ze2023-06-26T11:44:02.129Z35 km NNW of Beluga, AlaskaearthquakeNaN1.10NaNNaNautomaticakak
12023-06-26T11:28:01.160Z38.836498-122.8058322.071.33md21.080.00.012250.02ncnc739055512023-06-26T11:46:13.545Z7km WNW of Cobb, CAearthquake0.240.450.1622.0automaticncnc
22023-06-26T11:17:31.919Z61.607900-150.67340049.801.80mlNaNNaNNaN0.74akak02384wrwp32023-06-26T11:20:29.326ZSouthern AlaskaearthquakeNaN0.90NaNNaNautomaticakak
32023-06-26T11:12:49.040Z38.821167-122.7951662.671.08md25.033.00.011860.03ncnc739055462023-06-26T11:35:17.483Z6km NW of The Geysers, CAearthquake0.220.430.1622.0automaticncnc
42023-06-26T10:54:31.466Z61.408500-147.73640048.101.10mlNaNNaNNaN0.48akak02384weeju2023-06-26T10:55:46.949ZNaNearthquakeNaN0.80NaNNaNautomaticakak
52023-06-26T10:40:37.800Z36.921501-121.51733424.931.95md11.0140.00.070330.35ncnc739055412023-06-26T11:25:14.416Z9km NNE of San Juan Bautista, CAearthquake7.152.980.1310.0automaticncnc
62023-06-26T10:38:07.150Z36.328999-120.8998345.951.73md19.087.00.044710.05ncnc739055362023-06-26T11:15:15.373Z22km WSW of New Idria, CAearthquake0.280.620.084.0automaticncnc
72023-06-26T10:37:17.930Z36.909500-121.5504999.211.50md11.0122.00.041590.06ncnc739055312023-06-26T11:35:10.481Z7km N of San Juan Bautista, CAearthquake0.520.540.2610.0automaticncnc
82023-06-26T10:36:11.830Z36.838665-121.671501-0.341.49md6.0223.00.078470.15ncnc739055262023-06-26T11:09:11.348Z6km SSW of Aromas, CAearthquake1.6913.030.273.0automaticncnc
92023-06-26T10:23:32.380Z19.198500-64.61780034.003.78md8.0302.00.911400.24prpr20231770002023-06-26T11:01:02.983Z97 km N of Cruz Bay, U.S. Virgin Islandsearthquake3.5016.100.095.0reviewedprpr
timelatitudelongitudedepthmagmagTypenstgapdminrmsnetidupdatedplacetypehorizontalErrordepthErrormagErrormagNststatuslocationSourcemagSource
112082023-05-27T12:34:28.500Z36.385833-120.7058338.701.52md27.0142.00.175800.12ncnc738905902023-05-29T20:19:11.857Z4km SW of New Idria, CAearthquake0.240.650.21500024.0reviewedncnc
112092023-05-27T12:32:16.142Z59.023600-150.97610046.702.10mlNaNNaNNaN0.56akak0236rbc3y32023-06-01T14:35:53.899Z60 km SE of Port Graham, AlaskaearthquakeNaN0.60NaNNaNreviewedakak
112102023-05-27T12:31:32.860Z61.969400-149.85190034.301.40mlNaNNaNNaN0.69akak0236rbbxil2023-06-01T14:35:53.695Z21 km S of Susitna North, AlaskaearthquakeNaN1.00NaNNaNreviewedakak
112112023-05-27T12:28:49.051Z62.507100-151.51390095.802.60mlNaNNaNNaN0.68akak0236rbbboy2023-06-18T11:33:33.040Z38 km W of Petersville, AlaskaearthquakeNaN0.40NaNNaNreviewedakak
112122023-05-27T12:28:11.542Z61.689800-150.35450045.001.60mlNaNNaNNaN0.56akak0236rbb8qp2023-06-01T14:35:53.195Z17 km WSW of Willow, AlaskaearthquakeNaN1.00NaNNaNreviewedakak
112132023-05-27T12:25:15.332Z61.257700-151.88960087.201.30mlNaNNaNNaN0.29akak0236rbalxp2023-06-01T14:35:55.286Z45 km WNW of Beluga, AlaskaearthquakeNaN0.80NaNNaNreviewedakak
112142023-05-27T12:19:49.450Z44.228167-110.7521671.911.11md15.093.00.056990.14uuuu605410162023-05-30T14:20:18.620Z47 km ENE of Warm River, Idahoearthquake0.388.500.2550007.0revieweduuuu
112152023-05-27T12:17:12.820Z33.966833-116.66766713.940.59ml22.071.00.073360.06cici402350072023-05-30T20:12:39.473Z12km SW of Morongo Valley, CAearthquake0.170.480.07700011.0reviewedcici
112162023-05-27T12:05:35.610Z18.170333-67.3661678.122.68md9.0213.00.238500.13prpr714112682023-05-27T12:23:04.840ZMona Passageearthquake0.530.770.1170368.0reviewedprpr
112172023-05-27T11:58:44.607Z61.776000-150.0699009.901.20mlNaNNaNNaN0.66akak0236rawb802023-06-01T14:35:52.995Z3 km NNW of Willow, AlaskaearthquakeNaN0.40NaNNaNreviewedakak